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#POLARIS TRIAL#
Obtaining faithful information on the dynamic of the system can be particularly difficult, which is why it is generally more efficient to design systems that dynamically learn the best actions to play through trial and errors. In a distributed context it is also essential to design systems that can seamlessly adapt to the workload and to the evolving behaviour of its components (users, resources, network). Let us clarify how our research activities are positionned with respect to this trend.Ī first line of research in POLARIS is devoted to the use statistical learning techniques (Bayesian inference) to model the expected performance of distributed systems to build aggregated performance views, to feed simulators of such systems, or to detect anomalous behaviours. Building on our performance evaluation and distributed computing background, we obviously publish our work in conferences like SIGMETRICS, INFOCOM, CCGRID or IPDPS but we also regularly publish our work in major AI conferences like IJCAI, NeurIPS, ICLR, or ICML.
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ReDaS (Analysis Techniques and Workflow Methodologies for Reproducible Data Science) is an associated team with our colleagues from UFRGS in Porto Alegre, Brazil.ĪI and Learning is everywhere now.
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Distributed Optimization: Continuous Game Theory and On-line Distributed Optimization.Asymptotic Models: Local Interactions and Transient Analysis in Adaptive Dynamic Systems.Simulation: Fast and Faithful Performance Prediction of Very Large Systems.Analysis: Multi-Scale Analysis and Visualization.Measurement: Sound and Reproducible Experimental Methodology.The POLARIS team works in close cooperation with other research teams on a continuum of five research themes: Optimization: stochastic approximations, mean field limits, game theory, mean field games, primal dual optimization, learning, information theory.Modeling and Simulation: emulation, discrete event simulation, perfect sampling, Markov chains, Monte Carlo methods, ….Trace Analysis: parallel application visualization (paje, triva/viva, framesoc/ocelotl, …), characterization of failures in large distributed systems, visualization and analysis for geographical information system, spatio-temporal analysis of media events in RSS flows from newspapers, ….Experiment design: experimental methodology, measuring/monitoring/tracing tools, experiment control, design of experiments, reproducible research, in particular in the context of large computing infrastructures (grid, HPC, volunteer computing, embedded systems, …).Here are some slides presenting the team in a nutshell as well as a few recent results and our last Inria Activity Report.įrom our past experience, we gather skills in: The goal of the POLARIS project is to contribute to the understanding (from the observation, modeling and analysis to the actual optimisation through adapted algorithms) of the performance of very large-scale distributed systems such as supercomputers, cloud infrastructures, wireless networks, smart grids, transportation systems, or even recommendation systems.